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At the end of this entry, we will have seen.

Let's start!

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Neural Networks are capable of approximating mathematical functions. The use of various activation functions is what makes this versatile.

The Artificial Neuron is the most important element of a Neural Network. A Neural Network with the ability to perform complex regression and classification processes can be obtained by combining multiple neurons.

The use of an activation function in each of the neurons in the network is what makes this versatile.

It is important to remember that this function must satisfy an essential characteristic.

We will see the main activation functions used in Neural and Convolutional Networks, as well as Recurrent Networks, LSTM Networks, and Transformer Networks. The main characteristics, advantages and disadvantages of these models will be discussed in detail.

The function takes any range of values at the input and maps them to the range of 0 to 1 at the output. The following figure shows this behavior.

The main application of the activation function is really classification.

This is due to the saturation problem.

The function saturates to 1 when the input is very high and to 0 when the input is very low. The calculated gradients are small, which makes it difficult for the algorithm to converge during training.

The sigmoid has a similar behavior to this function, with the difference between the output values being between -1 and 1:

The function is also affected by the saturation problem, but it offers the advantage of having a symmetrical output which facilitates the training process.

The saturation problem limits the applicability of the activation function in Neural Networks.

The ReLU function solved this problem.

The acronym for rectified linear unit is English.

The following figure shows the behavior of the function.

When the input is negative and the output is positive, the output will be equal to zero.

The ReLU activation function has become the most used function in Deep Learning models.

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